Method and device for calculating optimal index of synthesis interval of remote sensing time series data

Abstract

The invention discloses a remote sensing time series data synthesis interval optimal index calculation method and device, and belongs to the technical field of remote sensing data processing. The method comprises the following steps: calculating an effective observation value proportion PVO which represents the proportion of effective observation values in all observation values in a synthesis time sequence under a given synthesis interval; calculating information loss (ILC), wherein the ILC represents the ratio of the number of effective observation values in the synthetic time sequence to the number of effective observation values in the original time sequence; calculating an optimal synthesis interval index OCII, wherein the OCII is expressed by a difference value between the normalized forms of the PVO and the ILC; according to the value of the OCII, the optimal synthesis interval is selected, and the optimal synthesis interval is the synthesis interval when the OCII reaches the maximum value. According to the method, the balance between the positive effect and the negative effect of synthesis interval increase can be well and quantitatively considered, so that an optimal synthesis interval suggestion is provided, and a better time sequence synthesis effect is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of remote sensing data processing, and in particular relates to a method and a device for calculating an optimal index of a synthetic interval of remote sensing time series data. Background Art

[0002] Remote sensing time series data plays an important role in monitoring the dynamic changes of the surface. However, due to cloud pollution and other interference factors, the time series data actually obtained often have problems of discontinuity and inconsistent time intervals. In the prior art, time synthesis methods are widely used to generate standard multi-day synthetic time series products, such as MODIS 16-day Normalized Difference Vegetation Index NDVI products and Copernicus Global Land Service (CGLS) Leaf Area Index (LAI) products. The process of remote sensing time series temporal synthesis is to move forward step by step through a fixed window, and select cloud-free and best quality observations in each time interval as synthetic data. However, the existing methods are usually based on empirical knowledge when determining the synthesis interval, and lack quantitative indicators to optimize the selection of the synthesis interval.

[0003] Among the existing technologies related to time series synthesis, the most common one is the synthesis method, that is, when there are multiple cloud-free original observations, all observations of each pixel in each synthesis interval are selected as the best observation value as the synthetic data. Maximum Value Composites (MVC), the best available pixel algorithm, the synthesis based on the maximum ratio of near-infrared and blue light bands (MAX-RNB), and the moving median synthesis are used to select the best observation value, but determining the synthesis interval is still a major challenge. For example, Maximum Value Composites (MVC) is a commonly used method in remote sensing time series synthesis. Its core idea is to select the maximum value of each pixel (such as NDVI or reflectance) as the synthetic value within a certain time window. This method assumes that the maximum value best represents the best state of surface vegetation and can usually effectively reduce the impact of noise such as clouds and shadows. Maximum value synthesis is simple and efficient, suitable for vegetation monitoring and phenological analysis, but it may be sensitive to extreme values ​​and easily affected by different interval lengths, and needs to be used in combination with quality control. The MAX-RNB synthesis algorithm selects the value with the largest ratio of near-infrared to blue light reflectance in each synthesis window, which increases the sensitivity to noise such as clouds and is less affected by data noise (clouds, cloud shadows, snow / ice).

[0004] At the same time, existing studies usually use a unified synthesis interval for time series synthesis without considering specific regions, and ignore the importance of selecting different synthesis intervals based on the characteristics of different regions. There is a lack of quantitative indicators to determine the optimal synthesis interval, resulting in inconsistent quality of synthetic time series data in different regions and seasons. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method and device for calculating the optimal index of synthesis interval of remote sensing time series data, which realizes the quantitative determination of the optimal synthesis interval index (OCII) to maximize the proportion of valid observations (PVO) and minimize the information loss (ILC) while ensuring high temporal resolution.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A method for calculating an optimal index of a synthesis interval of remote sensing time series data, the method comprising:

[0008] Step 1, calculate the effective observation ratio PVO, where PVO represents the ratio of effective observations to all observations in the synthetic time series under a given synthetic interval;

[0009] Step 2: Calculate the information loss ILC, where ILC represents the ratio of the number of valid observations in the synthetic time series to the number of valid observations in the original time series.

[0010] Step 3, calculating the optimal composite interval index OCII, wherein the OCII is represented by the difference between the normalized forms of PVO and ILC;

[0011] Step 4: Select an optimal synthesis interval according to the value of the optimal synthesis interval index OCII, where the optimal synthesis interval is the synthesis interval when the optimal synthesis interval index OCII reaches a maximum value.

[0012] In another aspect, the present invention provides a remote sensing time series data synthesis interval optimal index calculation device, comprising:

[0013] A PVO calculation module is used to calculate the effective observation ratio PVO, where the PVO represents the ratio of effective observations to all observations in a synthetic time series at a given synthetic interval;

[0014] An ILC calculation module, used for calculating the information loss ILC, wherein the ILC represents the ratio of the number of valid observations in the synthetic time series to the number of valid observations in the original time series;

[0015] An OCII calculation module, configured to calculate an optimal composite interval index OCII, wherein the OCII is represented by a difference between normalized forms of PVO and ILC;

[0016] The output module is used to select the optimal synthesis interval according to the value of the optimal synthesis interval index OCII, wherein the optimal synthesis interval is the synthesis interval when the optimal synthesis interval index OCII reaches a maximum value.

[0017] In a third aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned remote sensing time series data synthesis interval optimal index calculation method.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for calculating the optimal index of the synthesis interval of remote sensing time series data.

[0019] The beneficial effects of the present invention are:

[0020] The OCII provided by the present invention is based on the reality that the PVO and ILC increase rates are different due to the differences in image acquisition capabilities in different regions and seasons. The differences in cloud coverage and image acquisition capabilities in different regions and seasons are numerically considered using these two parameters, and the optimal synthesis interval suggestions can be provided for different regions. By measuring the relative changes between PVO and ILC, the balance between the positive effect (i.e., the increase in the proportion of valid observations) and the negative effect (i.e., the loss of temporal information) of the increase in the synthesis interval can be quantitatively considered, thereby providing the optimal synthesis interval suggestion and achieving a better effect of time series synthesis. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a schematic diagram of the optimal index calculation method for remote sensing time series data synthesis interval of the present invention;

[0022] Figure 2 The changes of normalized PVO, normalized ILC and OCII at different synthesis intervals;

[0023] Figure 3 Changes in F1-score for crop classification at different synthetic intervals for the three test sites;

[0024] Figure 4 Classification diagrams of the three test sites at different synthetic intervals, optimal intervals and large intervals. DETAILED DESCRIPTION

[0025] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0026] like Figure 1As shown, the present invention proposes a method for calculating the optimal index of the synthesis interval of remote sensing time series data, which is used to determine the optimal synthesis interval of remote sensing time series data. The optimal synthesis interval (OCII) is determined by calculating the trade-off between the proportion of valid observations (Proportion of ValidObservations, PVO) and information loss (Information Loss of Composite, ILC). OCII is an indicator used to determine the synthesis interval of optical reflectance / vegetation index time series data (such as the most common 8 days, 10 days, 16 days, 30 days or 60 days, etc.). It is assumed that a better synthesis interval should have a higher proportion of valid observations in the synthetic time series, and at the same time, the information loss caused by the synthesis operation is lower. Based on this assumption, the method specifically includes:

[0027] Step 1: Calculate the proportion of valid observations (PVO): At the pixel scale, for a synthetic time series with a given synthesis interval, we first need to calculate the proportion of valid observations to all observations (PVO) to indicate the quality level of the synthetic time series. The proportion of valid observations of the i-th pixel in the synthetic time series is denoted as , that is, the ratio of the effective observation value to the length of the synthetic time series:

[0028]

[0029] Where L is the number of synthetic intervals in the length of the time series at the predefined synthetic interval, represents the number of valid observations of the i-th pixel in the time series under the predefined synthesis interval. It can be imagined that when the predefined synthesis interval is large, a smaller number of synthesis intervals L may have a higher PVO value, because it is easier to search for valid observations in a larger synthesis window ( Figure 1 Middle view). For the study area, data availability is not the same for each pixel due to the random occurrence of clouds. To represent most areas, pixel-level The values ​​are averaged at the region level to generate region-level PVO values:

[0030]

[0031] Where N is the total number of pixels in the study area.

[0032] Step 2: Calculate the information loss (ILC); A wider synthesis window may result in a reduction in the number of synthesis intervals in the length of the composite time series, indicating a reduction in temporal resolution and potential information loss (ILC). The information loss caused by this time series synthesis process is quantified by comparing the number of valid observations in the time series at a predefined synthesis interval with the number of valid observations in the original time series ( Figure 1 The synthesis information loss of the i-th pixel is recorded as It is expressed as:

[0033]

[0034] in, represents the number of valid observations in the original time series. Similarly, The values ​​were averaged to generate regional-level ILC values.

[0035] Step 3: Calculate the optimal composite interval index (OCII); for a better composite interval, a higher proportion of valid observations (i.e., a larger PVO value) and less information loss (a smaller ILC value) are expected. However, these two aspects compete with each other and cannot achieve the optimal result at the same time. In order to consider the trade-off between PVO and ILC, the optimal composite interval index OCII is calculated by the median composite algorithm at the regional scale:

[0036] (4)

[0037]

[0038] Here, the normalized forms of PVO and ILC are used ( and ), because the value ranges of these two components are different, formula (5) represents the specific data normalization calculation method. PVO and ILC represent the positive and negative effects of increasing the synthesis interval, respectively. Therefore, as the synthesis interval increases, OCII first increases due to the larger effect of PVO, and then decreases due to the larger effect of ILC. The highest value of OCII usually appears in the middle part of the predefined possible interval set.

[0039] This ensures that all pixels in the study area have synthetic time series of consistent length, which is beneficial to the subsequent standardized post-processing and analysis (such as classification, fitting, filtering, change detection, etc.) of remote sensing synthetic time series in practical applications, and overcomes the errors caused by spatial differences.

[0040] Preferably, other synthesis algorithms, such as maximum value synthesis (MVC) or best available pixel algorithm, may be used instead of the median synthesis algorithm.

[0041] Step 4: According to the value of OCII, select the optimal synthesis interval. The optimal synthesis interval is the synthesis interval when OCII reaches the maximum value. When the subsequent remote sensing time series application is performed again according to the optimal synthesis interval index, it has a better effect. Taking the synthesis of remote sensing time series data for crop classification application as an example, it can have higher accuracy. Due to the differences in image acquisition capabilities in different regions and seasons, the PVO and ILC increase rates are different, and the synthesis interval corresponding to the top of the subtraction difference (OCII) sequence between the two is different.

[0042] For other application scenarios, when you need to pay more attention to PVO or ILC when using OCII to determine the optimal synthesis interval, you can consider increasing the ILC weight when calculating OCII. Or PVO weight ,as follows:

[0043] (6)

[0044] For example, in land cover change detection or surface disturbance monitoring applications, denser time series may be required, i.e. larger; nearly cloud-free images during a particular crop growing season may be crucial for crop classification, i.e. The determination of the weight depends on the specific circumstances, such as the time scale over which the disturbance occurs or the crop calendar.

[0045] Example

[0046] Since the dynamic changes of crop optical signals are more complex and therefore more sensitive to the synthesis interval, the effectiveness of OCII was tested at three research sites dominated by farmland. The first site mainly grows corn and spring wheat, the second site mainly grows corn and soybeans, and the third site mainly grows winter wheat and rapeseed in spring. The actual data availability of different sites is different, representing the raw data availability (number of cloud-free observations) from high to low; the dynamic complexity of the optical signal of each site is different, which is used in the example to illustrate the applicability of OCII under different sensitivity conditions.

[0047] PVO and ILC constitute the two components of OCII. Figure 2The variation patterns of PVO, ILC, and OCII with increasing synthesis intervals at the three test sites are shown. At the first site, the PVO value quickly reaches a high value of 0.8 at a 16-day synthesis interval and increases only slightly thereafter; however, the ILC value shows a different variation pattern, continuing to rise with increasing synthesis intervals until the 90-day interval. Therefore, for the Yili site, which has a higher proportion of sunny days, the peak of OCII occurs at the 16-day synthesis interval. At the second site, the PVO and ILC values ​​increase relatively evenly until the 25-day interval. Beyond this point, the increase in ILC exceeds that of PVO, indicating that the peak of OCII occurs at the 25-day interval. At the third site, the PVO value continues to increase due to limited availability of valid data, and after the 30-day interval, the increase in ILC exceeds that of PVO. Therefore, the OCII value reaches its highest value at 30 days. For the synthesis of Sentinel-2 time series data, OCII provides a simple and operational standard, suggesting that a smaller synthesis interval (16 days) be used for the first site, while larger synthesis intervals (25 days and 30 days) be used for the second and third sites, respectively.

[0048] For crop classification based on synthetic time series data with different intervals, a random forest (RF) model was used to conduct crop classification experiments based on synthetic time series. Considering the comparison of different synthetic intervals (i.e., 8, 10, 16, 20, 25, 30, 60, and 90 days), eight random forest classification scenarios were conducted based on the same crop samples at each test site. In order to avoid overfitting of the RF model, a sub-region to whole classification approach was adopted, in which the training samples were not randomly selected from the entire region, but from a specific sub-region. Then, the trained classifier was applied to the entire region to obtain the crop map. The accuracy verification was performed on the entire region. Since three sub-regions were selected at each site, one classification result could be obtained using each sub-region, and the final classification accuracy was calculated as the average of the three classification results accuracy indicators F1-score.

[0049] Figure 3 The classification accuracy (F1-score) of the main crops at different synthetic intervals for the three test sites is shown. Figure 4The classification maps of the three test sites at small intervals (8 days), optimal intervals, and large intervals (90 days) are shown. The median composite algorithm and the MAX-RNB algorithm were used to generate the composite values ​​within each interval. When the median composite algorithm was used, at the first site, the classification accuracy of the main crops (spring wheat and corn) was relatively high at the 16-day composite interval recommended by OCII, and dropped significantly when the composite interval was greater than 20-25 days. At the second site, when the 25-day composite interval recommended by OCII was used, the F1-score values ​​of corn and soybeans both reached the highest. At the third site, when the 30-day composite interval recommended by OCII was used, the classification accuracy of winter wheat and rapeseed was also relatively good. Similar results were obtained using the MAX-RNB composite algorithm ( Figure 3 Right column). When the second and third stations used smaller synthesis intervals (e.g., 8 days) or larger synthesis intervals (>60 days), the classification accuracy was lower than the classification accuracy under the synthesis interval recommended by OCII. Regarding the classification map, salt noise appeared in the classification map generated by the second station using 8-day synthesis images, and there was an obvious omission error in the classification map when using a 90-day synthesis interval. At the third station, rapeseed was misclassified as winter wheat when using a smaller synthesis interval (8 days) or a larger interval (90 days). The classification map recommended by OCII is more consistent with the reference map ( Figure 4 ). It can be seen that OCII effectively balances the relationship between the proportion of valid observations and the potential information loss, generating synthetic time series suitable for practical applications.

[0050] On the other hand, the present invention provides a remote sensing time series data synthesis interval optimal index calculation device, which includes various modules that can implement various steps of the above method, specifically including:

[0051] A PVO calculation module is used to calculate the effective observation ratio PVO, where the PVO represents the ratio of effective observations to all observations in a synthetic time series at a given synthetic interval;

[0052] An ILC calculation module, used for calculating the information loss ILC, wherein the ILC represents the ratio of the number of valid observations in the synthetic time series to the number of valid observations in the original time series;

[0053] An OCII calculation module, configured to calculate an optimal composite interval index OCII, wherein the OCII is represented by a difference between normalized forms of PVO and ILC;

[0054] The output module is used to select the optimal synthesis interval according to the value of the optimal synthesis interval index OCII, wherein the optimal synthesis interval is the synthesis interval when the optimal synthesis interval index OCII reaches a maximum value.

[0055] In a third aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned remote sensing time series data synthesis interval optimal index calculation method.

[0056] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for calculating the optimal index of the synthesis interval of remote sensing time series data.

[0057] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for calculating the optimal index of synthesis interval of remote sensing time series data, characterized in that: The method comprises: Step 1, calculate the effective observation ratio PVO, where PVO represents the ratio of effective observations to all observations in the synthetic time series under a given synthetic interval; Step 2: Calculate the information loss ILC, where ILC represents the ratio of the number of valid observations in the synthetic time series to the number of valid observations in the original time series. Step 3, calculating the optimal composite interval index OCII, wherein the OCII is represented by the difference between the normalized forms of PVO and ILC; Step 4: Select an optimal synthesis interval according to the value of the optimal synthesis interval index OCII, where the optimal synthesis interval is the synthesis interval when the optimal synthesis interval index OCII reaches a maximum value.

2. The method for calculating the optimal index of the synthesis interval of remote sensing time series data according to claim 1, characterized in that: The step 1 includes: the ratio of valid observation values ​​of the i-th pixel in the synthetic time series is recorded as : where L is the number of synthetic intervals in the length of the time series at a given synthetic interval, represents the number of valid observations of the i-th pixel in the time series at a given synthesis interval; The pixel level The values ​​are averaged at the regional level to generate regional PVO values: Where N is the total number of pixels in the study area.

3. The method for calculating the optimal index of the synthesis interval of remote sensing time series data according to claim 1, characterized in that: The step 2 includes the synthesis information loss of the i-th pixel It is expressed as: in, Represents the number of valid observations in the original time series; The pixel level The values ​​are averaged at the regional level to generate regional-level ILC values.

4. The method for calculating the optimal index of the synthesis interval of remote sensing time series data according to claim 1, characterized in that: The step 3 includes calculating the optimal composite interval index OCII by a median composite algorithm: （4） In the formula, They represent the normalized forms of the proportion of valid observations PVO and the information loss ILC respectively.

5. The method for calculating the optimal index of the synthesis interval of remote sensing time series data according to claim 1, characterized in that: The step 4 also includes assigning different weights to the normalized forms of PVO and ILC for calculation.

6. The method for calculating the optimal index of the synthesis interval of remote sensing time series data according to claim 1, characterized in that: The step 4 also includes that due to the differences in image acquisition capabilities in different regions and seasons, the PVO and ILC increase rates are different, and the synthesis intervals corresponding to the OCII are different.

7. The method for calculating the optimal index of the synthesis interval of remote sensing time series data according to claim 4, characterized in that: In step 3, the maximum value synthesis algorithm or the best available pixel algorithm may be used instead of the median synthesis algorithm.

8. A device for calculating the optimal index of synthesis interval of remote sensing time series data, characterized in that: include: A PVO calculation module is used to calculate the effective observation ratio PVO, where the PVO represents the ratio of effective observations to all observations in a synthetic time series at a given synthetic interval; An ILC calculation module, used for calculating the information loss ILC, wherein the ILC represents the ratio of the number of valid observations in the synthetic time series to the number of valid observations in the original time series; An OCII calculation module, configured to calculate an optimal composite interval index OCII, wherein the OCII is represented by a difference between normalized forms of PVO and ILC; The output module is used to select the optimal synthesis interval according to the value of the optimal synthesis interval index OCII, wherein the optimal synthesis interval is the synthesis interval when the optimal synthesis interval index OCII reaches a maximum value.

9. An electronic device, characterized in that: include: one or more processors; A memory for storing one or more programs; Wherein, when one or more programs are executed by the one or more processors, the one or more processors implement a method for calculating an optimal index of a synthesis interval of remote sensing time series data as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the method for calculating the optimal index of the synthesis interval of remote sensing time series data as described in any one of claims 1-7.

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